Bird collisions with aircraft pose a serious threat to human safety. However, broad-scale patterns in how bird strikes might vary through space and time have yet to be fully understood. Here, we conducted a biogeographical study of bird strikes to answer two questions: (1) Are bird strikes higher at certain times of the year in the Northern and Southern Hemispheres? and (2) Is seasonality in bird strikes more prominent in the Northern Hemisphere than in the Southern Hemisphere? We collated data on monthly bird strikes from 122 airports across the globe and used circular statistics to test for hemispherical asymmetries in the circular mean and variance in bird strikes. Results showed that annual peaks in bird strikes occurred between late summer to autumn seasons, and as a result, they occurred at opposite times of year in the northern and southern hemispheres. Results also showed that bird strikes were more seasonal in the Northern Hemisphere than the Southern Hemisphere, where strikes tended to occur more consistently throughout the year. Practical implications Overall results indicate that avian collisions with aircraft show strong biogeographic patterning, concomitant with global patterns in bird breeding seasons and migration tendencies. Methods We focused on bird strikes instead of all wildlife strikes since birds strikes have resulted in considerably more human fatalities (Avisure 2024), thus posing a larger threat to human safety than strikes with other groups. We searched the Google Web, Google Scholar, and Web of Science search engines for data on monthly bird strikes at airports using the keywords ‘bird strike’ with ‘airport’, ‘seasonal’, ‘monthly’ and ‘annual’. The language settings allowed for search results in any language. We included sources that reported bird strikes in each calendar month over a minimum of 12 months at individual airports. The data availability ranged from one year to 20 years (Table S1). Monthly data from studies conducted over multiple years were averaged for each month among years prior to analyses. Apart from online sources, data for Wellington Airport were obtained via personal communication with airport personnel. Studies that reported either yearly bird strikes for individual airports, or monthly bird strikes for entire countries did not match the spatial and/or temporal resolution of this study and hence were not included. Data on strikes with non-avian species (bats, non-volant mammals, reptiles) were also not included. We restricted our data sources to those available via web search for logistical reasons (except for the Wellington data, to which we had access prior to commencing the study). Given that time is a ‘non-linear’ variable, we used circular statistics to analyze the data (Berens 2009). In this instance, the circle is used to represent one cycle (i.e. one calendar year), and we analyze the timing of an event within this cycle (i.e. occurrences of high bird strikes, see Jammalamadaka and Sengupta 2001). Seasonality in bird strikes was assessed using the temporal scale of months of the year. Each month occupied 30° on the circular plot starting with January by convention and going clockwise. The monthly strike frequency data were visualized using circular plots called rose diagrams (circular frequency distributions). Seasons were classified generally as December-January-February being boreal winter and austral summer, March-April-May being boreal spring and austral autumn, June-July-August being boreal summer and austral winter, and September-October-November being boreal autumn and austral spring. For some airports, bird strikes were reported as number of strikes in each month, while others used ‘strike rates’ (typically strikes/10,000 flight movements) accounting for aircraft movements. We converted the strike frequencies to angles (in degrees) prior to analyses using the circular package in R to make them comparable across airports (Agostinelli and Lund 2022, R Core Team 2022). For each airport, we calculated two metrics to characterize the annual distribution of bird strikes: 1) angle of mean vector (in degrees, 0°-360°), a measure of central tendency, to identify the time of year with overall high bird strikes (referred to in this study as timing of ‘peak strikes’), and 2) circular standard deviation, a measure of dispersion of the bird strikes around the mean (as per Ting et al. 2008). These values were then used to calculate the overall angles of mean vector and circular standard deviations to identify the month with the highest bird strikes for the Northern and Southern Hemispheres. The angle of the mean vector represents the timing of peak strikes for each airport and corresponds to the direction of the vector arrow on the circular plot. Circular standard deviation represents the spread of the strikes around the mean, and is inversely related to the length of the vector (see Wright and Calderon 1995, Ting et al. 2008). A high circular standard deviation would indicate that the strikes are more dispersed around the mean, while a low circular standard deviation would indicate that strikes are less dispersed around the mean, and thus, more seasonal. To answer the first question of whether bird strikes were higher at certain times of the year, we used the angles of mean vector for each airport to calculate the overall angles of mean vector and circular standard deviations for the Northern and Southern Hemispheres. The month of peak bird strikes for each hemisphere was determined by back-calculating the angle of mean vector to the corresponding month (January for values between 0° and 30°, February between 30° and 60°, and so on). Rayleigh’s Test of Uniformity was conducted to assess if the strikes were uniformly distributed throughout the year for each hemisphere. The means of the distributions of strikes in both hemispheres were compared using Watson-William’s Test of Homogeneity of Means (see Pewsey et al. 2013). To answer the second question of whether the annual spread of bird strikes differed between hemispheres, we conducted linear regression analyses to assess the relationship of circular standard deviations at individual airports with latitude in both hemispheres. Additionally, we conducted Welch’s t-test on the circular standard deviations between hemispheres to test for overall hemispherical differences in annual dispersions of strikes. Analyses and visualizations were conducted in the R environment using circular and ggplot2 packages, respectively (Wickham 2016, Agostinelli and Lund 2022, R Core Team 2022).

Code and data from: Measuring the overall functional diversity by aggregating its multiple facets: functional richness, biomass evenness, trait evenness, and dispersion

https://doi.org/10.5061/dryad.tmpg4f55r

This document explains the structure of the provided files and the methods presented in the associated article by Wojcik et al (2024), "Measuring the overall functional diversity by aggregating its multiple facets: functional richness, biomass evenness, trait evenness, and dispersion", Methods in Ecology and Evolution. First, we describe the files uploaded in the folder called generated_and_caseStudy_data, which are presented datasets in .csv and .xlsx formats, and second, we describe the files, software, methods, and data in the folder scripts_Kindex used for the published study. In both folders, empty cells or NA values stand for "not available", since computations could not be done due to methodological details in the publicatio...

In this work we present results of all the major global models and normalise the model results by looking at changes over time relative to a common base year value. We give an analysis of the variability across the models, both before and after normalisation in order to give insights into variance at national and regional level. A dataset of harmonised results (based on means) and measures of dispersion is presented, providing a baseline dataset for CBCA validation and analysis. The dataset is intended as a goto dataset for country and regional results of consumption and production based accounts. The normalised mean for each country/region is the principle result that can be used to assess the magnitude and trend in the emission accounts. However, an additional key element of the dataset are the measures of robustness and spread of the results across the source models. These metrics give insight into the amount of trust should be placed in the individual country/region results. Code at https://doi.org/10.5281/zenodo.3181930

Cumulative median and mean (with margin of error for the 95% confidence intervals) of isolations, quarantines, and infections across all simulations, grouped by network type and the intervention strategy deployed.